code: def _compute_updated_scores(
      hypos: List[Hypothesis],
      next_token_probs: torch.Tensor,
      beam_width: int,
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      hypo_scores = torch.tensor(
          [_get_hypo_score(h) for h in hypos]
      ).unsqueeze(1)
      nonblank_scores = (
          hypo_scores + next_token_probs[:, :-1]
      )  # [beam_width, num_tokens - 1]
      (
          nonblank_nbest_scores,
          nonblank_nbest_idx,
      ) = nonblank_scores.reshape(-1).topk(beam_width)
      nonblank_nbest_hypo_idx = nonblank_nbest_idx.div(
          nonblank_scores.shape[1], rounding_mode="trunc"
      )
      nonblank_nbest_token = (
          nonblank_nbest_idx % nonblank_scores.shape[1]
      )
      return (
          nonblank_nbest_scores,
          nonblank_nbest_hypo_idx,
          nonblank_nbest_token,
      )
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 11 for calling a global function with example usage: _compute_updated_scores(hypos, next_token_probs, beam_width) and returns: nonblank_nbest_scores,  nonblank_nbest_hypo_idx,  nonblank_nbest_token